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Space-time machine learning models for valuation and prediction of real estate objects Space-time machine learning models for valuation and prediction of real estate objects

Space-time machine learning models for valuation and prediction of real estate objects

The goal of this project is to develop novel machine learning and statistical models for both hedonic mass appraisal of real estate objects and for scenario-based real estate price prediction.

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Fakten

Projektart

Forschung

Beteiligte interne Organisationen

Institut für Finanzdienstleistungen Zug IFZ

Externe Projektfinanzierer

Innosuisse

Finanzierung

Innosuisse - HSLU als Hauptforschungspartnerin (Main Research Partner)

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Beteiligte Personen intern

Projektleiter/in

Fabio Sigrist

Projektmitarbeiter/in

Publikationen

Artikel, Rezension; peer reviewed (1)
- Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2020). Maximum likelihood estimation of spatially varying coefficient models for large data with an application to real estate price prediction. Spatial Statistics, 1. doi: 10.1016/j.spasta.2020.100470
Artikel, Rezension; not peer reviewed (2)
- Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (2021). Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models. arXiv, 1.
- Dambon, Jakob; Fahrländer, Stefan; Karlen, Saira; Lehner, Manuel; Schlesinger, Jaron; Sigrist, Fabio & Zimmermann, Anna (2020). Examining the Vintage Effect in Hedonic Pricing using Spatially Varying Coefficients Models: A Case Study of Single-Family Houses in the Canton of Zurich. preprint, 1.
Sonstige Publikationsformate (1)
- Dambon, Jakob; Sigrist, Fabio & Furrer, Reinhard (11.10.2019). varycoef: Modeling Spatially Varying Coefficients [Softwareprogramm]. https://cran.r-project.org/web/packages/varycoef/index.html
Präsentation (Tagungsbeitrag/Referat/Vortrag) (3)
- Dambon, Jakob (19.06.2020). varycoef: Modeling Spatially Varying Coefficients. eRum 2020, virtual.
- Dambon, Jakob (25.10.2019). varycoef:An R Package to Model Spatially Varying Coefficients. Swiss Statistics Seminar, Bern (Switzerland).
- Dambon, Jakob (09.11.2018). Spatially Varying Coefficients Models: A Comparison of Maximum Likelihood Estimators with other Estimators. Swiss Statistics Seminar, Bern (Switzerland).

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Space-time machine learning models for valuation and prediction of real estate objects

Kurzinformation

Fakten

Beteiligte Personen intern

Publikationen

Artikel, Rezension; peer reviewed (1)

Artikel, Rezension; not peer reviewed (2)

Sonstige Publikationsformate (1)

Präsentation (Tagungsbeitrag/Referat/Vortrag) (3)